Classification of single particles by neural networks based on the computer-controlled scanning electron microscopy data

1997 ◽  
Vol 348 (1-3) ◽  
pp. 375-388 ◽  
Author(s):  
Philip K. Hopke ◽  
Xin-Hua Song
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Scanning Electron Microscopy ◽  
Scanning Electron Microscopy Data ◽  
Single Particles ◽  
Electron Microscopy Data ◽  
Computer Controlled ◽  
Microscopy Data ◽  
Scanning Electron

Fractographic Regularities in Fatigue Failure of 17Mn1Si Steel

2014 ◽  
Vol 1040 ◽  
pp. 230-235
Author(s):  
Pavlo Maruschak ◽  
Sergey Panin ◽  
Ilya Vlasov ◽  
Iryna Danyliuk ◽  
Roman Bishchak
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Scanning Electron Microscopy Data ◽  
Structural Level ◽  
Deformation And Failure ◽  
Leading Role ◽  
Electron Microscopy Data ◽  
Microscopy Data ◽  
Scanning Electron

Using the scanning electron microscopy data the main regularities of the fatigue crack propagation in the 17Mn1Si steel were studied. Based on fracture surface observation and analysis one can testify that the transition of the leading role of deformation and failure from the lower structural level to the higher one has the ordered pattern.

scholarly journals Creating a virtual device for processing the results of sorption measurements in the study of zinc oxide nanorods

2020 ◽  
Vol 7 (4) ◽  
pp. 154-158
Author(s):  
E. V. Maraeva ◽  
N. V. Permiakov ◽  
Y. Y. Kedruk ◽  
L. V. Gritsenko ◽  
Kh. A. Abdullin
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Zinc Oxide ◽  
Zinc Oxide Nanorods ◽  
Scanning Electron Microscopy Data ◽  
Virtual Device ◽  
Electron Microscopy Data ◽  
Oxide Nanorods ◽  
Microscopy Data ◽  
Scanning Electron

The work is devoted to the creation of a virtual device (computer program) for processing the results of sorption analysis of nanomaterials, including for estimating the size of nanoparticles based on the specific surface area. The obtained evaluation results were compared with the scanning electron microscopy data. Photocatalytically active zinc oxide samples were chosen as the object of the study.

scholarly journals Interactive web analysis and presentation of computercontrolled scanning electron microscopy data

1969 ◽  
Vol 20 ◽  
pp. 103-106
Author(s):  
Peter Riisager ◽  
Nynke Keulen ◽  
Uffe Larsen ◽  
Roger K. McLimans ◽  
Christian Knudsen ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Web Application ◽  
Grain Morphology ◽  
Data Sets ◽  
Scanning Electron Microscopy Data ◽  
Global Database ◽  
Computer Controlled ◽  
Microscopy Data ◽  
Scanning Electron

In the following we describe the result of the Titan Project, an interactive web application (Titan) developed at the Geological Survey of Denmark and Greenland (GEUS) together with DuPont Titanium Technologies. The main aim of Titan is to make computer-controlled scanning electron microscopy (CCSEM) data, generated at GEUS, available via the internet. In brief, CCSEM is a method automatically to detect particles with a scanning electron microscope (SEM), and based on computer-controlled imagery to measure the chemistry and grain morphology of each particle in a given sample (Knudsen et al. 2005; Bernstein et al. 2008); Keulen et al. 2008. Titan makes data available on-line so that the user can interact with the data sets and analyse them using a web browser. In addition to CCSEM data, Titan contains a global database of titanium deposits and various reports. The web application is customised, such that the functionality and amount of data available for a given user depend on the privileges of that user.

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics

2020 ◽  
Vol 26 (3) ◽  
pp. 403-412 ◽  
Author(s):  
Pavel Potocek ◽  
Patrick Trampert ◽  
Maurice Peemen ◽  
Remco Schoenmakers ◽  
Tim Dahmen
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Scanning Electron Microscopy ◽  
Image Reconstruction ◽  
Data Acquisition ◽  
Real Life ◽  
Large Field ◽  
Acquisition Time ◽  
Scanning Electron Microscopy Data ◽  
Scanning Electron

AbstractWith the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to electron radiation. Random sparse scanning can be used in the combination with image reconstruction techniques to reduce the acquisition time or electron dose in scanning electron microscopy. In this study, we demonstrate a workflow that includes data acquisition on a scanning electron microscope, followed by a sparse image reconstruction based on compressive sensing or alternatively using neural networks. Neuron structures are automatically segmented from the reconstructed images using deep learning techniques. We show that the average dwell time per pixel can be reduced by a factor of 2–3, thereby providing a real-life confirmation of previous results on simulated data in one of the key segmentation applications in connectomics and thus demonstrating the feasibility and benefit of random sparse scanning techniques for a specific real-world scenario.

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